Lightning Qubit device

The lightning.qubit device is an extension of PennyLane’s built-in default.qubit device. It uses a custom-built backend to perform fast linear algebra calculations for simulating quantum state-vector evolution.

A lightning.qubit device can be loaded using:

import pennylane as qml
dev = qml.device("lightning.qubit", wires=2)

Supported operations and observables

Supported operations:

BasisState	Prepares a single computational basis state.
CNOT	The controlled-NOT operator
CRot	The controlled-Rot operator
CRX	The controlled-RX operator
CRY	The controlled-RY operator
CRZ	The controlled-RZ operator
Hadamard	The Hadamard operator
PauliX	The Pauli X operator
PauliY	The Pauli Y operator
PauliZ	The Pauli Z operator
PhaseShift	Arbitrary single qubit local phase shift
ControlledPhaseShift	A qubit controlled phase shift.
QubitStateVector	Prepare subsystems using the given ket vector in the computational basis.
Rot	Arbitrary single qubit rotation
RX	The single qubit X rotation
RY	The single qubit Y rotation
RZ	The single qubit Z rotation
S	The single-qubit phase gate
T	The single-qubit T gate

Supported observables:

Hadamard	The Hadamard operator
Identity	The identity observable \(\I\).
PauliX	The Pauli X operator
PauliY	The Pauli Y operator
PauliZ	The Pauli Z operator

Parallel adjoint differentiation support:

The lightning.qubit device directly supports the adjoint differentiation method, and enables parallelization over the requested observables (Linux/MacOS support only).

To enable parallel differentiation over observables, ensure the OMP_NUM_THREADS environment variable is set before starting your Python session, or if already started, before importing packages:

# Option 1: Before starting Python
export OMP_NUM_THREADS=4
python <your_file>.py

# Option 2: Before importing packages
import os
os.environ["OMP_NUM_THREADS"] = 4
import pennylane as qml

Assuming you request multiple expectation values from a QNode, this should automatically parallelize the computation over the requested number of threads. You should ensure that the number of threads does not exceed the available physical cores on your machine.

If you are computing a large number of expectation values, or if you are using a large number of wires on your device, it may be best to limit the number of expectation value calculations to at-most OMP_NUM_THREADS concurrent executions. This will help save memory, at the cost of additional compute time. To enable this, initialize a lightning.qubit device with the batch_obs=True keyword argument, as:

# Before importing packages
import os
os.environ["OMP_NUM_THREADS"] = 4
import pennylane as qml
dev = qml.device("lightning.qubit", wires=2, batch_obs=True)

devices

 

Download Python script

 

Download Notebook

 

View on GitHub